Organizational structure identification using a Hidden Markov Random Field model and a novel algorithm for Quadratic Assignment Problem

Abstract

In this paper, we employ a hidden Markov random field (HMRF) model and a novel algorithm for the quadratic assignment problem (QAP) to discover the attributes of and relationships among organizational members, assets, mission areas, and mission tasks. The problem is one of identifying the mapping between the hypothesized nodes of a command and control (C2) organization and tracked individuals and resources. The HMRF formulation allows the computation of the posteriori energy function quantifying the belief that the observed data graph has been generated by a particular organizational graph (model graph). The experimental results demonstrate that the HMRF probabilistic model and the m-best assignment-based search algorithm can accurately identify the different organizational structures and achieve correct node mappings among various organizational members. The algorithm itself can be employed for solving general QAPs as well.